In order to overcome these problems and make the lane detection in autonomous driving an industrial-grade product application, more recent research [1]}, [2]}, [3]}, [4]}, [5]} have focus on the more complex 3D lane perception domain. 3D-LaneNet [1]} proposes an end-to-end approach from 2D image space to orthographic bird 's eye view space (BEV). The method unifies 2D image feature encoding, spatial transformation and 3D lane extraction. Recent sota work, persformer [5]} proposes a framework that unifies the detection tasks of 2D lane lines and 3D lane lines. The work also proposes spatial transformation module,Perspective Transformer to obtain excellent 3D lane detection, and also publish a large-scale real scene based 3D dataset, OpenLane. The achievements of these work are remarkable and have shown that it is extremely feasible to regress 3D lane detection directly from monocular images using a single model.
